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The most practical method for monitoring forest change over large areas is using remotely sensed data. However, given that current techniques are somewhat weak for monitoring small-scale forest disturbances, achieving accurate monitoring remains challenging, especially in tropical areas where selective and illegal logging occurs frequently. To further improve the ability to monitor forest changes, we estimated tree canopy cover (TCC) using Sentinel-1 and Sentinel-2 data. We developed an approach to monitor forest change on the obtained TCC time series. This approach was applied to monitor forest change in the Bago Mountains of Myanmar from 2017 to 2021. We then completed accuracy assessments and area estimation using reference data obtained from stratified random sampling and unbiased estimators. The final results indicated that: (1) in TCC estimation, Sentinel-1 played a limited role; the red-edge bands of Sentinel-2 achieved slightly different results to the other bands, and superior results were obtained by using all bands; (2) our method successfully mapped forest change with the overall accuracy of 93%. Furthermore, compared with the most widely used and the most recent approaches, our method was better at capturing forest disturbances. [ABSTRACT FROM AUTHOR]

Vegetation indices (VIs) were used to detect when and where vegetation changes occurred. However, different VIs have different or even diametrically opposite results, which obstructed the in-depth understanding of vegetation change. Therefore, this study examined the spatial and temporal consistency of five VIs (EVI; NBR; NDMI; NDVI; and NIRv) in detecting abrupt and gradual vegetation changes, and provided an ensemble algorithm which integrated the change detection results of the five indices to reduce the uncertainty of change detection using a single index. The spatial consistency of the five indices in abrupt change detection accounted for 50.6% of the study area, but the temporal consistency was low (21.6%). Wetness indices (NBR, NDMI) were more sensitive to negative abrupt changes, greenness indices (EVI, NDVI, NIRv) were more sensitive to positive abrupt changes; and both types of indices were similar in detecting gradual and total changes. The overall accuracy of the ensemble method was 81.60% and higher than that of any single index in abrupt change detection. This study provides a comprehensive evaluation of the spatial and temporal inconsistencies of change detection in model-fitting errors and various types of vegetation changes. The proposed ensemble method can support robust change-detection. [ABSTRACT FROM AUTHOR]

Mapping of deforestation, forest degradation, and recovery is essential to characterize country-level forest change and formulate mitigation actions. Previous studies have mainly used a simple forest/non-forest classification after forest disturbance to identify deforestation and forest degradation. However, a more flexible approach that is applicable to different forest conditions is desirable. In this study, we examined an approach for mapping deforestation, forest degradation, and recovery using disturbance types and tree canopy cover estimates from annual Landsat time-series data from 1988 to 2020 across Cambodia. We developed models to estimate both disturbance types and tree canopy cover based on a random forest algorithm using predictor variables derived from a trajectory-based temporal segmentation approach. The estimated disturbance types and canopy cover in each year were then used in a rule-based classification of deforestation, forest degradation, and recovery. The producer's and user's accuracies ranged from 59.1% to 72.9% and 60.8% to 91.6%, respectively, for the forest change classes of mapping deforestation, forest degradation, and recovery. The approach developed here can be adjusted for different definitions of deforestation, forest degradation, and recovery according to research objectives and thus has the potential to be applied to other study areas. [ABSTRACT FROM AUTHOR]

The Upper Guinean Forest region of West Africa, a globally significant biodiversity hotspot, is among the driest and most human-impacted tropical ecosystems. We used Landsat to study forest degradation, loss, and recovery in the forest reserves of Ghana from 2003 to 2019. Annual canopy cover maps were generated using random forests and results were temporally segmented using the LandTrendr algorithm. Canopy cover was predicted with a predicted-observed r2 of 0.76, mean absolute error of 12.8%, and mean error of 1.3%. Forest degradation, loss, and recovery were identified as transitions between closed (>60% cover), open (15–60% cover) and low tree cover (< 15% cover) classes. Change was relatively slow from 2003 to 2015, but there was more disturbance than recovery resulting in a gradual decline in closed canopy forests. In 2016, widespread fires associated with El Niño drought caused forest loss and degradation across more than 12% of the moist semi-deciduous and upland evergreen forest types. The workflow was implemented in Google Earth Engine, allowing stakeholders to visualize the results and download summaries. Information about historical disturbances will help to prioritize locations for future studies and target forest protection and restoration activities aimed at increasing resilience. [ABSTRACT FROM AUTHOR]